Unsupervised invertible physics-based vector representation for molecules

ABSTRACT

The Artificial Intelligence engine can perform one or more operations. A query can be submitted to the Artificial Intelligence engine to search directly for a set of targeted properties for an unnamed molecule having the set of targeted properties. An indication of a structure of one or more candidate molecules found to have the set of targeted properties with the Artificial Intelligence engine is generated by applying one or more machine learning algorithms. The indication of the structure of the one or more candidate molecules found to satisfy the set of targeted properties in 3-dimensional space is supplied to a user in response to the query for the set of targeted properties to the Artificial Intelligence engine.

CROSS-REFERENCE

This application claims priority under 35 USC 119 to U.S. provisionalpatent application Ser. No. 63/043,480, titled “Unsupervised invertiblephysics-based vector representation for molecules,” filed 24 Jun. 2020,which the disclosure of such is incorporated herein by reference in itsentirety.

TECHNICAL FIELD

An embodiment of a concept herein relates to techniques and tools inArtificial Intelligence computing.

BACKGROUND

As known, a diamond and graphite are two molecules with the samechemical composition, but extremely different physical properties foreach molecule. In both, carbon is joined by covalent bonds, but ingraphite the carbon atoms form sheets that are weakly bonded together.In diamond, the carbon forms a three-dimensional framework. Isomers arean example class of molecules that have the same molecular formula butdifferent molecular geometries giving them different molecularproperties for the same molecular formula.

Accordingly, generating new compounds in different applications (e.g.drug discovery, novel fuel components, etc.) requires expensive cyclesof designing molecules with geometries that may give desiredcharacteristics, synthesizing the molecule in a chemistry lab, andtesting the molecule to determine whether it has the targetedproperties. Most Artificial Intelligence (AI) techniques have nostandard use cases today for performing the task of generating newcompounds with certain properties. For example, a typical AI techniquecan merely learn representations from molecules for which a propertythat users desire to predict is already known (i.e., from labeledmolecules), which is not much more than a database correlation of anamed molecule to its known properties; rather than, looking at thestructures making up that molecule and then correlating each structureand/or combination of structures to the one or more properties resultingor likely to result for any molecule, named or unnamed, that containsthe same or chemically very similar set of atoms and their geometrywithin a given molecule.

SUMMARY

Provided herein are various methods, apparatuses, and systems for anArtificial Intelligence based reasoning engine and explaining itsreasoning process.

In an embodiment, the Artificial Intelligence engine can be composed oftwo or more modules that use one or more machine learning models. Themolecule property predictor module cooperates with a molecule vectormodule to predict molecular properties from a molecule[semantic-dependent] vector field in a 3-dimensional space. An inputmodule cooperates with the molecule vector module, the molecule propertypredictor module, and an atom vector module, to allow these modules toapply the one or more machine learning models to allow a user 1) toinput two or more atoms and have a first machine learning model in themolecule property predictor module output a set of properties for aresulting molecule formed by the two or more atoms, as well as 2) tosupply an input of a set of targeted properties for an unlabeledmolecule and have the first machine learning model output a set of atomsand a geometric structure for the unlabeled molecule. In an embodiment,a labeled molecule is data that comes with a tag or other meta data toassist in identifying the molecule, like a name, a type, or a number. Anunlabeled molecule is data that comes with no tag.

In an embodiment, the Artificial Intelligence engine can perform one ormore operations. A query can be submitted to the Artificial Intelligenceengine to search directly for a set of targeted properties for anunnamed molecule having the set of targeted properties. An indication ofa structure of one or more candidate molecules found to have the set oftargeted properties with the Artificial Intelligence engine is generatedby applying one or more machine learning algorithms. The indication ofthe structure of the one or more candidate molecules found to satisfythe set of targeted properties in 3-dimensional space is supplied to auser in response to the query for the set of targeted properties to theArtificial Intelligence engine. These and many more embodiments arediscussed below.

DRAWINGS

FIG. 1 illustrates an embodiment of a diagram of an example high levelrepresentation of a system with an Artificial Intelligence engine thatcan receive an input of a set of targeted properties for an unnamedmolecule from the user, and then output candidate molecules likely tohave those set of targeted properties;

FIG. 2 illustrates an embodiment of a diagram of a more detailed examplesystem with an Artificial Intelligence engine that uses an unsupervised,invertible, physics-based, vector representation for generating,testing, and predicting molecules;

FIG. 3 illustrates an embodiment of a diagram of an example ArtificialIntelligence engine to find a candidate molecule that is found to likelysatisfy the set of targeted properties;

FIG. 4 illustrates an embodiment of an example block diagram of anexample Artificial Intelligence engine composed of two or more modulesthat use one or more machine learning models trained to do multipleoperations including generating representations of geometric structuresof molecules;

FIG. 5 illustrates an embodiment of an example block diagram of anexample Artificial Intelligence engine and example modules with inputsdirectly into an atom vector module and/or a molecule vector module;

FIG. 6 illustrates an embodiment of an example block diagram of examplemolecular properties of a molecule, under analysis, to values ofstructural properties for that molecule under analysis;

FIG. 7 illustrates an embodiment of a diagram of an example atom vectormodule configured to cooperate with one or more transformers to map atomvectors to a 3-dimensional space;

FIG. 8 shows a mapping of atom vectors to a 3-dimensional space as wellas mapped to molecule vectors to the 3-dimensional space with a samecoordinate system to allow for a proper comparison of the atom vectorsto the molecule vectors because a molecule is a 3-dimensional object andan arrangement of the atoms within that 3-dimensional space;

FIG. 9 illustrates an embodiment of a diagram of an example input modulethat is configured to receive and supply information from one or moreknowledge sources, including at least information from a chemistryexpert, to act as one or more constraints to adapt the machine learningto find a candidate molecule vector field to agree with an atom vectorfield in order to fit the constraints created by the information;

FIG. 10 illustrates an embodiment of a diagram of an example ArtificialIntelligence engine recognizing patterns of structures in molecules sothat molecular properties can be correlated to structural properties inthe molecule;

FIG. 11 illustrates an embodiment of a diagram of an example ArtificialIntelligence engine that can generate physics-based, vectorrepresentations for testing and predicting molecules;

FIG. 12 illustrates a diagram of a number of electronic systems anddevices communicating with each other in a network environment inaccordance with an embodiment of the Artificial Intelligence engine; and

FIG. 13 illustrates a diagram of an embodiment of one or more computingdevices that can be a part of the systems associated with the ArtificialIntelligence engine discussed herein.

While the design is subject to various modifications, equivalents, andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and will now be described in detail. Itshould be understood that the design is not limited to the particularembodiments disclosed, but—on the contrary—the intention is to cover allmodifications, equivalents, and alternative forms using the specificembodiments.

DESCRIPTION

In the following description, numerous specific details can be setforth, such as examples of specific data signals, named components,number of models, etc., in order to provide a thorough understanding ofthe present design. It will be apparent, however, to one of ordinaryskill in the art that the present design can be practiced without thesespecific details. In other instances, well known components or methodshave not been described in detail but rather in a block diagram in orderto avoid unnecessarily obscuring the present design. Further, specificnumeric references such as the first server, can be made. However, thespecific numeric reference should not be interpreted as a literalsequential order but rather interpreted that the first server isdifferent than a second server. Thus, the specific details set forth canbe merely exemplary. The specific details can be varied from and stillbe contemplated to be within the spirit and scope of the present design.The term “coupled” is defined as meaning connected either directly tothe component or indirectly to the component through another component.

The Artificial Intelligence engine, such as a Deep Adaptive SemanticLogic Network, has a highly flexible architecture with its modules thatcaptures a molecular structure in 3D space. An example Deep AdaptiveSemantic Logic Network is described in PCT application number PCT/US18/31645 entitled, “Deep Adaptive Semantic Logic Network” filed May 8,2018, and the contents of, which are incorporated by reference hereinits entirety.)

Overall, this document will initially discuss a specific task performedby the machine learning networks in the modules to give the reader acomprehension of this design. Later, this document will discuss multipleexample tasks that this design can support and perform.

FIG. 1 illustrates an embodiment of a diagram of an example high levelrepresentation of a system with an Artificial Intelligence engine thatcan receive an input of a set of targeted properties for an unnamedmolecule from the user, and then output candidate molecules likely tohave those set of targeted properties. A multi-parameter ArtificialIntelligence model as part of the Artificial Intelligence engine 100 cansupply a candidate molecule with a set of targeted properties to themodules in the structural generation stage (e.g. the agreementcomparator module, atom vector module, molecule vector module, and 3Dvector field module). The Artificial Intelligence engine 100 can thencorrelate that molecule and its structure back to the atoms and theirstructure that would make up that candidate molecule with the set oftargeted properties. Note a candidate molecule can be a labeledmolecule, an unlabeled molecule, and/or a molecule within a set ofmolecules containing a combination of both unlabeled and labeledmolecules. Next, integrated synthesis and testing can be performed forthe atoms and their structure that would make up that candidate moleculewith the set of targeted properties to see if an optimizedhigh-performance molecule having those set of targeted properties can becreated.

FIG. 2 illustrates an embodiment of a diagram of a more detailed examplesystem with an Artificial Intelligence engine that uses an unsupervised,invertible, physics-based, vector representation for generating,testing, and predicting molecules.

A query can be submitted to the Artificial Intelligence engine 100 tosearch directly for a set of targeted properties for an unnamed moleculehaving the set of targeted properties. An indication of a structure ofone or more candidate molecules found to have the set of targetedproperties is generated with the Artificial Intelligence engine 100 byapplying one or more machine learning algorithms. The indication of thestructure of the one or more candidate molecules found to satisfy theset of targeted properties in 3-dimensional space is supplied to a userin response to the query for the set of targeted properties to theArtificial Intelligence engine 100.

The system can include i) a chemistry knowledge, ii) structured data,such as data on a geometric structure of a molecule, that can be used astraining data, iii) extraction tools from chemistry literature, whichcan be used in the training data, iv) an input from a molecular designersuch as molecule design/target properties requirements, v) aphysics-based simulation, vi) a synthesis planning process, vii) anautomated flow synthesis and purification system, viii) an automatedtesting system, and ix) and a feedback loop to generate an optimizedhigh-performance molecule. These components all cooperate to enable thedesign of synthesizable compounds from a set of targeted properties.

The training data extraction and enhancement can come from severalsources such as i) a public database extraction, ii) manually enteredfrom, for example, an expert in the field, iii) extracted from chemistryliterature, and iv) utilizing internal knowledge from previous resultsfrom the Artificial Intelligence engine 100, etc.

The physics-based simulation information can, for example, supplyinformation about the molecules and how valences and bonds affectmolecule properties, etc.

The Artificial Intelligence engine 100 can predict molecular propertiescomputationally, reducing the number of iterations through the physicaldesign-make-test cycle. This can greatly reduce the number of iterationsa synthetic chemist would have to go through to arrive at an optimizedcompound.

The candidate molecule with its geometric structure generated can gothrough the integrated synthesis and testing, which is fed back througha feedback loop into the machine learning networks of the ArtificialIntelligence engine 100. Thus, the synthesis and testing can occur withclosed-loop feedback driven by artificial intelligence. An examplefeedback loop in this process can start with the user. The user, in thisexample, a molecule design expert, supplies their desired information(e.g. a set of target properties in a molecule). The ArtificialIntelligence engine 100 and its trained modules apply the machinelearning process to find one or more candidate molecules and theirstructure as well as then output the atoms and their structure composingthe candidate molecule. The output module for the ArtificialIntelligence engine 100 provides a representation of the candidatemolecule to the synthesis planning process module (?) as well as to theautomated flow synthesis and purification process modules (?). Thecandidate molecule can go through automated testing and then supply theresults of the testing back to the Artificial Intelligence engine 100 toupdate the Artificial Intelligence models and their networks in themodules. Eventually, this feedback loop and design process can generatean optimized high-performance molecule.

FIG. 3 illustrates an embodiment of a diagram of an example ArtificialIntelligence engine to find a candidate molecule that is found to likelysatisfy the set of targeted properties.

The Artificial Intelligence engine 100 reconstructs i) a 3-dimensionalvector field of a structure of a first candidate molecule found thatsatisfies the set of targeted properties ii) with a structure of atomvectors from one or more atoms in a same 3-dimensional space.

The Artificial Intelligence engine 100 can have logic to writestructure-based rules that capture many examples correctly and thenapply that knowledge to extend the structure knowledge to real and/orhypothetical molecules that have not been trained on. The ArtificialIntelligence engine 100 can recognize moiety/part of a moleculeassociated with a given function to the example functional groups ofatoms causing Moiety 1, Moiety 2, and Moiety 3. (For example, see FIG.11 ) In this example, moiety recognition with the machine learningnetworks is applied to ligand model to examples corresponding to threedifferent functional groups of various atoms within a structure of amolecule.

Training and General Construction and Operation of the ArtificialIntelligence Engine Including its Modules

FIG. 4 illustrates an embodiment of an example block diagram of anexample Artificial Intelligence engine composed of two or more modulesthat use one or more machine learning models trained to do multipleoperations including generating representations of geometric structuresof molecules. FIG. 5 illustrates an embodiment of an example blockdiagram of an example Artificial Intelligence engine and example moduleswith inputs directly into an atom vector module and/or a molecule vectormodule.

An example embodiment of the Artificial Intelligence engine 100 composedof two or more modules can include, for example, a molecule vectormodule, one or more 3D vector field modules, a molecule propertypredictor module, an atom vector module, an atom properties module, aninput module 104 that can include an atom and molecule encoder module aswell as an input pathway to most of the other modules, and an outputmodule that collects outputs from most of the other modules in order topresent those outputs to a user on, for example, a display screen. Thoseinputs and output for each module can help a user understand the machinelearning reasoning within the Artificial Intelligence engine 100.

The input module 104 can cooperate with the molecule vector module, themolecule property predictor module, and the atom vector module, to allowthese modules to apply one or more machine learning models to allow auser to input various information (For example, see FIG. 2 ) and havethe Artificial Intelligence engine 100 perform multiple operations. Forexample, a user can 1) input two or more atoms and have a machinelearning model in the molecule property predictor module output a set ofproperties for a resulting candidate molecule formed by the two or moreatoms, as well as 2) supply an input of a set of targeted properties foran unlabeled/unnamed molecule and have the machine learning model outputa set of atoms and a geometric structure for the unlabeled molecule. Themodules can cooperate to look at the structures making up a molecule,under analysis, (as well as the inverse—a set of properties desired) andthen correlate each structure and/or combination of structures over tothe one or more properties resulting or likely to result for anymolecule, named or unnamed, that contains the same or chemically verysimilar set of atoms and their geometric structure within a givenmolecule.

An agreement comparator module can have a machine learning model trainedto compare an atom vector field to a molecule vector field in a3-dimensional space and determine when they are approximately a same.Note, the geometric structure of a molecule is formed by the set ofatoms and their arrangement in the 3-dimensional space. (For examplestructures see FIG. 7 , FIG. 8 , and FIG. 11 )

A molecule property predictor module is configured to cooperate with amolecule vector module to bilaterally correlate molecular properties tomolecule vector values.

An atom properties module is configured to cooperate with an atom vectormodule to bilaterally correlate atom properties to atom vector values sothat an output layer of the atom properties module will produce i) anidentity of an atom and ii) values of the atom properties for a suppliedatom vector value.

Overall, the Artificial Intelligence engine 100 can build and traineither 1) a multiple input parameter machine learning model and/or 2)multiple machine learning models, (e.g. one per stage of this process.)

The modules need to train the machine learning, such as neural networks,on their tasks. Several specific tasks need to be learned. Arbitrarily,one can discuss training of the machine learning on the atom side(stage 1) of this Artificial Intelligence engine 100, then training ofthe machine learning on the molecule side (stage 2) of this ArtificialIntelligence engine 100, and then training of the machine learning on acorrelation between a structure of the atom side and a structure of themolecule side (stage 3).

Stage 1 training on the machine learning on the atom side discusses abilateral correlation of properties of an atom to a structure of theatom in 3-dimensional space. Stage 2 training on the machine learning onthe molecule side discusses a bilateral correlation of properties of amolecule to a specific structure in the molecule causing that propertyin 3-dimensional space. Stage 3 training on the machine learningdiscusses a bilateral correlation of a structure of a molecule in3-dimensional space to one or more structures of sets of atoms and theirarrangement in 3-dimensional space.

FIG. 6 illustrates an embodiment of an example block diagram of examplemolecular properties of a molecule, under analysis, to values ofstructural properties for that molecule under analysis by having amolecule semantic dependent vector field in each space of the moleculeunder analysis in the 3-dimensional space.

Note, some general discussion on molecular properties and structure of amolecule follows. The machine learning between i) the atom vector moduleand the atom properties module as well as ii) the molecule vector moduleand molecule property predictor module have an understanding of thesebasic theories and relationships. Molecules are made up of atoms heldtogether by covalent bonds, etc., in specific positions relative to oneanother, with a spatial distribution of its atoms, etc. As a result,each molecule has a relatively defined geometric structure. In amolecule, the atoms are in 3D space allowing a molecule to be formed inmultiple dimensional ways with essentially the same atoms. For example,rotations of bonds (e.g. different atoms can connect in differentgeometric arrangements resulting in different bond angles). Again,isomers are an example class of molecules that have the same molecularformula but different molecular geometries giving them differentmolecular properties for the same molecular formula.

Molecular properties of a molecule can include a very large amount ofdifferent properties, but some example properties can include stability,solubility, toxicity, inhibition, aromaticity, dipole moment, solvation,conformers, excited states, spectroscopic characteristics (IR/UV), etc.The molecular properties can be associated with the geometric structureof that molecule.

Next, referring back to FIG. 4 , after the three basic tasks in stages1-3 of training are learned by the machine learning network, such asneural networks, Long Short Term Memory Networks, Deep Belief Networks,Random Forest, etc., in the modules of the Artificial Intelligenceengine 100, then multiple inner-connected tasks can be trained on andlearned by the machine learning networks in the modules of theArtificial Intelligence engine 100. Note, the text in paragraph 0044discussed training stages 1-3, and the text that follows will discussadditional stages of training. For example, stage 4 below discussestraining of the machine learning networks in the modules 1) to receivean input of a set of atoms and then on the output module 2) to output i)a resulting molecule including the structure of the molecule in3-dimensional space, ii) a composition of atoms in the resultingmolecule, as well as iii) a predicted set of properties for theresulting molecule, resulting from the inputted set of atoms. Stage 5discusses training of the machine learning networks in the modules 1) toreceive an input of a molecule and then on the output module 2) tooutput i) a resulting composition of atoms for the molecule includingeach atom's arrangement/position in 3-dimensional space, and ii) a setof properties for each of the atoms and/or grouping of atoms, resultingfrom the inputted molecule. Stage 6 discusses training of the machinelearning networks in the modules 1) to receive an input of a set of atomproperties and then on the output module 2) to output i) a resultingmolecule including the molecule's structure in 3-dimensional space, ii)a composition of atoms for the resulting molecule, as well as iii) a setof properties for the resulting molecule, resulting from the inputtedset of atom properties. Stage 7 discusses training of the machinelearning networks in the modules 1) to receive i) an input of a set ofdesired/targeted properties for an unnamed molecule and then on theoutput module 2) to output i) a resulting composition of atoms for themolecule with the desired/targeted set of properties as well as ii) eachatom's arrangement/position in 3-dimensional space for the molecule withthe desired/targeted properties, resulting from the inputted a set ofdesired/targeted properties. Other example operations the ArtificialIntelligence engine 100 can be trained on and then later performed willnaturally be understood from the examples in stages 4-7.

Step 0. For stages 1 and 3, a user through a user interface of the inputmodule 104 can supply training atoms with known values of atomproperties as well as a known geometric structure for that atom in3-dimensional space to the atom vector module. The atom vector modulecan also reference information sources, such as a database, chemistryliterature, a physics simulator, etc. (For example, see FIG. 2 ) toobtain the geometric arrangement of that atom being learned in3-dimensional space. Either way, the atom vector module will representthe known atom with its known geometric arrangement of that atom in3-dimensional space in vector format.

Likewise, for stages 2 and 3 training molecules with known values ofmolecule properties as well as a known geometric structure for thatmolecule in 3-dimensional space can be initially supplied to themolecule vector module by a user through the input module 104. Eitherway, the molecule vector module will represent the known molecule withits known geometric arrangement of that molecule in 3-dimensional spacein vector format. Similarly, the molecule vector module can alsoreference information sources, such as a database, chemistry literature,a physics simulator, etc. (For example, see FIG. 2 ) to obtain thegeometric arrangement of that molecule being learned in 3-dimensionalspace. In addition, an atom and molecule encoder can assist in providingthe atom and molecule information.

Step 1. Stage 1 training on the machine learning on the atom sidediscusses a bilateral correlation of properties of an atom to anarrangement/position of the atom in 3-dimensional space.

The atom vector module is configured to correlate an atom under analysisto atom vectors for that atom that reflect an identity of the atomitself as well as the known arrangement/position (e.g. geometricstructure) of the atom in 3-dimensional space. The atom vector module isconfigured to apply an algorithm to bilaterally correlate an atom andits known arrangement/position in 3-dimensional space to atom vectorvalues for that atom. A portion of an example vector value for an atomcan include (X, Y, Z) coordinates/point (e.g. 3, 5, 7) in a 3D plane. Aportion of an example vector value for an atom can include an identityof the atom, such as a carbon atom, oxygen atom, etc. A portion of anexample vector value for an atom can include other geometric structureinformation with respect to other atoms such as a single bonded carbonatom, a double bonded carbon atom, bond angles, etc.

The atom vector module is configured to cooperate with the atomproperties module. The atom properties module is configured to correlatean atom to values of atom properties for that atom. The atom propertiesmodule can have a machine learning network in an AI model, such as aneural network, a Long Short Term Memory Network, a Deep Belief Network,etc. that learns to correlate an atom being learned to values of atomproperties for that atom. The atom vector module provides, for an atombeing learned, atom vectors for that atom which reflect an identity ofthe atom itself as well as the known position/arrangement of the atom in3-dimensional space to the atom properties module. For training, both i)an identity of the atom, being learned, is known as well as ii) anarrangement/position of the atom in 3-dimensional space is known, fromits atom vector value. An input layer of the machine learning model isfed the atom vector value for the atom being learned and, for example,20 million examples of training data of atoms and their atom propertiesare learned for all of the potential properties associated with the atombeing learned as well as values associated with the atom properties. Themachine learning model can employ unsupervised learning algorithms totrain on and/or put under analysis when deployed to use unclassified andunlabeled data, the unsupervised learning algorithm attempts to uncoverpatterns from the data. The machine learning model can employ supervisedlearning algorithms to train on and/or put under analysis when deployedto use supervised learning with examples along with their labels ortargets for each example. The labels in the data help the learningalgorithm to correlate the features. The machine learning network canalso train on and then use some forms of reinforcement learning. In anembodiment, during training, no expert knowledge/rules or constraintsare applied. Rather, the Artificial Intelligence engine 100 isconfigured to construct a multiple input machine learning model(including neural networks) and/or multiple machine learning modelstrained initially based on statistical data-driven learning withunlabeled training data and no constraints allowing for betterrecognition of patterns of atoms and/or group of atoms and possiblemolecules beyond merely the examples used in training.

The learning by the machine learning network can occur because thecorrect properties and values for the atom having that geometricstructure in 3-dimensional space are known target values, for the atombeing learned. Thus, the target values of the properties are known andthe machine learning model predicts these target values for all of theatom properties. Thus, the coefficients and feature weights of thenetwork in the machine learning model are adjusted until the atomproperties module achieves a very high accuracy in predicting propertiesof the atom being learned and their values. The training continues untilthe machine learning model in the atom properties module can produce thecorrect values for all of the properties for an atom, such as charges,valency, etc., having that geometric structure in 3-dimensional space(e.g. the atom being learned but also any other atoms with a similargeometric structure in 3-dimensional space). From the examples, themachine learning network can identify and recognize patterns. Thus, themachine learning model learns to recognize patterns, parameters, values,and then to predict and effects associated with those patterns,parameters, values. The machine learning model naturally cancharacteristically generalize this understanding to atoms with similarstructure patterns that may not have been trained on.

For each training atom, iteratively, the atom vector under training, isthen fed from the atom vector module through the atom properties moduleuntil the, for example, neural network of the machine learning model canaccurately predict the actually known values of the labeled and/orunlabeled training atom. Atom vectors and atom properties can be trainedtogether with known values of atom properties for a given atom. Thispart of the Artificial Intelligence engine 100 is now trained so thatwhen the Artificial Intelligence engine 100 is in deployment, then atomvector values from an atom and/or functional group of atoms, underanalysis, can be put into the already trained atom properties module.The already trained atom properties module then accurately predictsknown values of atom properties including what type of atom this is,such as a carbon atom, and other properties known about that carbon atomincluding its geometric structure in 3D space. (e.g. X, Y, Zcoordinates/point in a 3D plane, type of valence bond, bond angles,etc.). During deployment, the already trained atom properties module andthe atom vectors module bilaterally correlate. Note, an example use ofthe trained atom properties module and the atom vectors module is toapply them to the 3D vector embedding space so as to aid in coming upwith the molecule(s) w/properties. 1) The input of desired atomproperties can be supplied to an input layer of the trained atomproperties module and an output layer of the atom vector value modulewill produce an atom vector value (that conveys the atom and itsgeometric structure in 3D space) that has those desired atom properties.2) One or more atoms and/or atom vector values conveying a geometricarrangement of the atom or atoms in 3D space can be supplied to an inputlayer of the atom vector value module and an output layer of the trainedatom properties module will produce the atom and the values of the atomproperties for those inputted atoms and/or atom vector values.

Step 2. Stage 2 training on the machine learning on the molecule sidediscusses a bilateral correlation of properties of a molecule to astructure of the molecule in 3-dimensional space.

The molecule vector module is configured to correlate a molecule underanalysis to molecule vectors for that molecule that reflect an identityof the molecule itself as well as the known geometric structure of themolecule in 3-dimensional space. The molecule vector module isconfigured to bilaterally correlate a molecule and its known geometricstructure in 3-dimensional space to molecular vector values for thatmolecule.

The bilateral correlation of properties of a molecule to a structure ofthe molecule in 3-dimensional space is trained and constructed similarlyto stage 1 for the atom but uses molecules instead.

The molecule vector module is configured to cooperate with the moleculeproperty predictor module.

Again component wise, the molecule property predictor module isconfigured to correlate a molecule to values of molecule properties forthat molecule. The molecule property predictor module can have a machinelearning networks in an AI model such as a neural network, a Long ShortTerm Memory Network, a Deep Belief Network, etc. that learns tocorrelate a molecule being learned to values of molecule properties forthat molecule. The molecule property predictor module cooperates withthe molecular vectors to learn to map the molecular vectors of amolecule under analysis to target properties for this molecule.

In the training, the learning can occur because the correct propertiesand values for the molecule having that geometric structure in3-dimensional space are known target values, for the molecule beinglearned. Thus, the target values of the properties are known and themachine learning model predicts these target values for all of theproperties of the molecule. Thus, the coefficients and feature weightsof the network in the machine learning model are adjusted until theproperty predictor module achieves a very high accuracy in predictingproperties of the molecule being learned and their values. The trainingof, for example, the machine learning model, such as a neural network,for the property predictor continues until it can accurately predict theactually known values of the labeled training molecule from itsmolecular vector value. After this training, all of the weights of theneural networks in this AI model are now locked down. The trainingcontinues until the machine learning model in the property predictormodule can produce the correct values for all of the properties for themolecule, such as solubility, toxicity, etc., having that geometricstructure in 3-dimensional space (e.g. the molecule being learned butalso any other molecules with a similar geometric structure in3-dimensional space). Again, a characteristic of the machine learning,the machine learning model learns to recognize patterns and thenparameters, values, and effects associated with those patterns. See FIG.8 's discussion on recognizing structure patterns within a molecule. Themachine learning model naturally can characteristically generalize thisunderstanding to molecules with similar structure patterns that may nothave been trained on. For each training molecule, iteratively, themolecule vector under training is fed from the molecule vector modulethrough the molecule property predictor module until the, for example,neural network of the machine learning model can accurately predict theactually known values of the labeled training molecule. This part of thesystem is now trained so that when the AI engine is in deployment, thenmolecular vector values from a molecule under analysis can be put intothe already trained property predictor module. The already trainedproperty predictor module then accurately predicts known values ofproperties including what is a geometric structure in 3D space for thatmolecule. During deployment, the already trained molecule propertypredictor module and the molecule vectors module bilaterallycorrelate. 1) The input of desired properties for a molecule can besupplied to an input layer of the trained molecule property predictormodule and an output layer of the molecular vector module will produce amolecular vector value (that conveys the molecule and its geometricstructure in 3D space) that has those desired properties for themolecule. 2) Molecules, via the molecule encoder, and/or molecularvector values that convey a geometric structure of the molecule in 3Dspace can be supplied to an input layer of the atom vector value moduleand an output layer of the trained molecule property predictor modulewill produce the molecule, if known, and the values of the moleculeproperties for the inputted molecule and/or molecular vector values.

Step 3. Stage 3 training on the machine learning discusses a bilateralcorrelation of a structure of a molecule in 3-dimensional space to astructure of an atom in 3-dimensional space.

An agreement comparator module uses a 3-dimensional field to couple anatom embedding to the molecule embedding. A 3D vector field moduleapplies a 3D vector algorithm to the atom vector values to generate asemantic vector field in 3D space. The agreement comparator modulecooperates with the 3D vector field module and the atom vector module togenerate a property vector for the atom being learned at every point in3D space. This creates the atom's embedding for the atom being learned.Iteratively for each atom being learned, the agreement comparator modulecooperates with the 3D vector field module and the atom vector module togenerate a property vector for each atom being learned at every point in3D space.

Next, the 3D vector field cooperates with molecular vectors from themolecular vector module and the agreement comparator module to generatea property vector for the molecule being learned in 3D space. After allof the vectors for all of the atoms learned are understood by theagreement comparator module, then the agreement comparator module cantrain with machine learning algorithms to compare whether one or moreatom vector fields 106 to a molecule vector field in a 3D dimensionspace and determine if they are approximately a same. Note, a structureof a molecule is formed by atoms in the 3D dimension space. The one ormore 3D vector field modules transform molecular vector values and atomvector values to ensure these values are compared in a same 3D space andcoordinate space. Thus, the agreement comparator module, molecularvector module, and 3D vector field module iteratively cooperate tosupply a molecule vector field in 3D space, for a molecule being learneduntil the network in the machine learning model of the agreementcomparator module can repeatedly accurately match up a molecule vectorfield to corresponding atom vector fields 106.

Each stage of the Artificial Intelligence engine 100, (e.g. theagreement comparator module cooperation with its corresponding modules,the atom properties module cooperation with the atom vector module, themolecule property predictor module cooperation with the molecule vectormodule, and the atom and molecule encoder with the atom vector moduleand the molecule vector module, during training of the machine learningthe agreement comparator module runs through, for example, at least twomillion examples of representative molecules in a vector space.

The training of these three stages can all occur in parallel. Afterthis, then just a little more training is required for the three stagesto work cooperatively during training and when deployed for operation.

Step 4. The modules can be trained (and thus later when deployed) 1) toreceive an input of a set of atoms via the atom and molecule encoder andthen 2) from the molecule property predictor module to present in theoutput module i) a resulting molecule, ii) a composition of atoms in theresulting molecule, iii) a set of properties for the resulting molecule,including the structure of the molecule in 3-dimensional space, and iv)any combination of these three, resulting from the inputted set ofatoms.

A set of unlabeled or labeled atoms can be inputted via the atom andmolecule encoder module and through the atom vector module to the atomproperties module. Each module performs its train and/or coded functionas discussed above. The atom properties module will output the knownvalues of atom properties for the inputted atoms. The agreementcomparator module, the 3D vector field module, and the atom vectormodule will create a semantic 3D vector field for all of the atom vectorvalues. The molecule vector module and the 3D vector field and agreementcomparator module will iteratively cooperate via a learning algorithm,such as Gradient Descent, to determine one or more molecules with amolecule vector field 108 that approximately matches (e.g. agrees) withthe atom vector field 106 in 3D space, and thus, the one or morematching molecular vector values. The molecular vector module inputs themolecular vector value of a candidate molecule that matches with theatom vector field 106 in 3D space to the property predictor module,which performs its function to output what the molecular properties willbe for the molecule resulting from the input atoms and/or likelyresulting molecules.

Step 5. In a reverse flow through the modules, the modules can betrained (and thus later when deployed) 1) to receive an input of amolecule via the atom and molecule encoder and then 2) from the atomproperties module to present in the output module i) a resultingcomposition of the atoms for that molecule, ii) a set of properties foreach of the atoms, including each atom's arrangement/position in3-dimensional space, and iii) any combination of these two, resultingfrom the inputted molecule.

Step 6. The modules can be trained (and thus later when deployed) 1) toreceive an input of a set of atom properties to the atom propertiesmodule and then 2) from the molecule property predictor module topresent in the output module i) a resulting molecule, ii) a compositionof atoms for the resulting molecule, iii) a set of properties for theresulting molecule including the molecule's structure in 3-dimensionalspace, and iv) any combination of these three, resulting from theinputted set of atom properties.

Step 7. The modules can be trained (and thus later when deployed) 1) toreceive an input of a set of desired/targeted properties for a moleculeto the molecule property predictor module and then 2) from the atomproperties module to output present in the output module i) a resultingcomposition of atoms for the molecule with the desired/targeted set ofproperties ii) each atom's arrangement/position in 3-dimensional spacefor the molecule with the desired/targeted properties, and anycombination of these two, resulting from the inputted set ofdesired/targeted properties.

When these three stages have completed training to work cooperatively,then the Artificial Intelligence engine 100 and its modules that havebeen trained and coded to perform their functions cooperatively can bedeployed for use in the field.

Additional Details and Examples

The Artificial Intelligence engine 100 that has been already trained hasan input module 104 for performing operations including an inversedesign on targeted properties of a molecule. An input module 104 isconfigured to accept, via a user interface and/or API, one or moretargeted molecular properties by a user and request that the trainedmodules of the Artificial Intelligence engine 100 cooperate to conductany of the example operations below. In an example, the input module 104can supply the information to the atom and molecule encoder, directly tothe atom vector module, and/or directly to the molecule vector module,directly to atom properties module and/or molecule property predictormodule, and any combination of these.

In deployment, the Artificial Intelligence engine 100 and its one ormore already trained machine learning models in the modules of theArtificial Intelligence engine 100 can perform example operations, suchas the four operations A.-D. below.

A. In deployment, an example machine learning process implemented in theArtificial Intelligence engine 100 can be as follows.

In a first approach, 1) an input module 104 can receive an input of aset of desired/targeted properties for an unlabeled molecule from auser, which can be supplied to a molecule property predictor module asan output target to achieve, 2) where the molecule vector module istrained to iteratively supply candidate molecular vector values into theproperty predictor module until one or more candidate molecular vectorvalues are found that satisfy the supplied targeted properties for theunlabeled molecule, and then 3) the atom properties module is configuredto supply to an output module, i) a resulting composition of atoms for amolecule found that is likely to have the desired/targeted set ofproperties, ii) an indication of each atom's arrangement/position in3-dimensional space for the molecule found with the desired/targetedproperties, and iii) any combination of these two, resulting from theset of desired/targeted properties inputted to the input module 104.Note, the unlabeled molecule's current identity or composition is notsupplied to the Artificial Intelligence engine 100.

In another approach, 1) the input module 104 can receive an input of aset of desired/targeted properties for an unlabeled molecule from auser, which is supplied directly to the molecule property predictormodule, 2) a reverse flow process through the property predictor moduleyields one or more candidate molecular vector values to start anadaptive process on the finding one or more molecules that satisfy thesupplied targeted properties for the unlabeled molecule, and then 3) theatom properties module is configured to supply to an output module i) aresulting composition of atoms for a molecule found that is likely tohave the desired/targeted set of properties, ii) an indication of eachatom's arrangement/position in 3-dimensional space for the moleculefound with the desired/targeted properties, and iii) any combination ofthese two, resulting from the inputted set of desired/targetedproperties. In this approach, a reverse-flow of information occurs inthe molecule property predictor module, which can cooperate with themolecule vector module to supply initial candidate molecular vectorvalues that contain most if not all of the set of targeted properties asa starting point.

In the first approach, a logical flow of the process can be as follows.The input module 104 can receive an input of a set of desired/targetedproperties for an unlabeled molecule (whose current identity orcomposition is not supplied to the Artificial Intelligence engine 100).

The local structure of a molecule can be obtained by using theArtificial Intelligence engine 100's approach of searching for one ormore embeddings that satisfy the target properties' criteria via the useof a machine learning algorithm iteratively going through possibleembeddings rather than learning a mapping from input features toembeddings.

The machine learning model in the Artificial Intelligence engine 100 canstart with an initial candidate of a molecular vector value.

The molecule vector module can supply an initial candidate of amolecular vector value based on many factors, such as i) expertintuition (e.g. a chemist), cross-referencing internal knowledge, ii) adatabase, of previously tested and/or learned molecular vector valuesthat have been correlated to one or more of the target properties for amolecule under analysis, and iii) other knowledge sources—e.g. physicsand chemistry literature. Note, a user may also manually input aninitial candidate molecule, which is encoded to a molecular vector valuethrough an atom and molecule encoder cooperating with the moleculevector module.

The machine learning model in the Artificial Intelligence engine 100 canfind one or more molecular vector values that satisfy achieve/satisfythe set of targeted properties for an unlabeled molecule. The moleculevector module feeds each candidate molecular vector value to the alreadytrained molecule property predictor module, which outputs a resultingmolecule and the properties of that resulting molecule based on itsstructure. A comparison can be made to see if the resulting molecule andthe properties of that resulting molecule do or do not satisfy/achievethe set of targeted properties for the unlabeled molecule submitted bythe user. The Artificial Intelligence engine 100 is configured to adaptmolecular vector values, starting with the initial candidate molecularvector value, until one or more instances of the candidate molecularvector values achieve/satisfy the set of targeted properties for theunlabeled molecule. Again, the Artificial Intelligence engine 100 canadapt molecular vector values from the initial candidate molecularvector value by iteratively creating and submitting molecular vectorvalues through the molecule vector module to the molecule propertypredictor that is already trained with its feature weights andcoefficients locked down. The molecule vector module and the moleculeproperty predictor can adapt instances of molecular vector values, viaiteration through, for example, gradient descent. During the machinelearning process to find a molecular vector value thatsatisfies/achieves the target properties for the unlabeled molecule, theArtificial Intelligence engine 100 can factor in chemistry knowledge andother forms of knowledge to act as constraints to adapt instances of themolecular vector value until a molecular vector value is found toachieve/satisfy the target properties for the unlabeled molecule. Nowthat the machine learning model in the Artificial Intelligence engine100 has found a molecular vector value for the unlabeled molecule thatcan achieve the target properties, the machine learning process may nowadvance to the next stage.

The Artificial Intelligence engine 100 can generate a 3D vector fieldfor the molecule that was found to satisfy the target properties for theunlabeled molecule. Once a candidate molecular vector value is found forthe unlabeled molecule that achieves the desired target properties, thenthe 3D vector field module is configured to transform and map themolecular vector value for the unlabeled molecule that achieves thedesired target properties, from the molecular vector module into avector field in 3D space.

Next, the atom vector module and the 3D vector field cooperate togenerate a property vector at every point in the 3D space. The agreementcomparator module is already trained with machine learning algorithms tocompare an atom vector field 106 to a molecule vector field 108 in a3-dimensional space and determine if they are approximately the same.Note, a structure of a molecule is formed by atoms in a 3-dimensionalspace.

The 3D vector field is configured to work with the atom vector valuesand/or molecular vector values to put these vector values into the same3D coordinate space. This captures the global structure of the moleculeby locating atoms in 3-dimensional space and projecting their embeddingsto generate a continuous 3-dimensional embedding vector field. Theagreement comparator module is already trained with machine learningalgorithms with their feature weights and coefficients locked down tolearn how to map molecule vectors to atom vectors so that they areapproximately the same. For example, the machine learning model in theagreement comparator module can compare molecule vectors of thecandidate molecule to the molecule of the in this over X amount ofvectors, such as 5 vectors in the exact same 3D space. (See for exampleFIG. 8 )

The Artificial Intelligence engine 100 can find matching atom vectorvalues. Once the Artificial Intelligence engine 100 has created thesemantic molecule vector field for the unlabeled molecule, now theagreement comparator module and the atom vector module can cooperate tofind atom vector values for atoms that match the molecule vector field108 for the unlabeled molecule in 3D space. The agreement comparatormodule and the atom vector module then cooperate to learn the atomembeddings to reconstruct the embeddings of the molecule vector field108.

The atom vector module, the 3D vector field module, and the agreementcomparator module cooperate to perform an iterative machine learningprocess to find one or more candidate atom vector fields 106 thatapproximately agree with the molecule vector field 108 in 3D space. Theatom vector module can put in a candidate atom vector value and thenuse, for example, a gradient descent iteration, until an atom vectorvalue is found that at least comes close to matching the semantic vectorfield in the 3D space from the molecular vector value that has the setof target properties. The machine learning with candidate instances ofatom vectors learns the particular atom vector values that match upapproximately with the molecule vectors from the unlabeled molecule thatmeet the set of target properties in 3D space. This captures the globalstructure of the unlabeled molecule.

Note, the different stages of the machine learning can incorporate priorknowledge about structure. In deployment, chemist intuition, physicsknowledge, and physical knowledge can act as constraints to adaptlearned atom vector structures, and relations between substructures tomolecule vectors in the Agreement 3D space to fit chemist intuitionconstraints and physics knowledge constraints. The chemist intuition,physics knowledge, and physical knowledge can help with choosing, forexample, an initial instance of atom vectors to start with, which basedon this knowledge has a better chance to match up rather than a randominstance of atom vector values. The chemist intuition, physicsknowledge, and physical knowledge also can act as constraints written inas rules speed up and focus the machine learning of these atom vectorvalues that approximately match up the molecule vectors.

In an embodiment, the agreement comparator module and the atom vectormodule adapt the atom vector representations and the molecule vectorrepresentations to fit chemist intuition and physical knowledge, whichcan be graphed into a network understandable by the machine learningmodel.

Next, the atom vector values for the atoms matching up with moleculevectors in the 3D space have been found. The atom vector module thenfeeds the atom vector values to the already trained atom propertiesmodule. The atom properties module maps the atom vector values to atomproperties. The atom properties module is configured to present in theoutput module i) a resulting arrangement of atoms for a molecule that isfound to likely to have the desired/targeted set of properties ii) anindication of each atom's arrangement/position in 3-dimensional spacefor the molecule with the desired/targeted properties, and iii) anycombination of these two, resulting from the inputted set ofdesired/targeted properties.

These Artificial Intelligence methods can generate new compounds. TheArtificial Intelligence engine 100 can search directly for an embeddingof an unlabeled molecule having such properties dictated by what theuser wants to target. This by itself enables the Artificial Intelligenceengine 100 to be unsupervised (i.e., learned from unlabeled data). Themodules of the Artificial Intelligence engine 100 can cooperate topresent a representation of a molecule found to satisfy the user'stargeted properties with both the local and global structure of thatmolecule and that molecule can be learned from unlabeled molecules.

B. The modules can cooperate to perform an operation 1) to receive aninput of a set of atom properties to the atom properties module and then2) from the molecule property predictor module to present in the outputmodule i) a resulting molecule, ii) a composition of atoms for theresulting molecule, iii) a set of properties for the resulting moleculeincluding the molecule's structure in 3-dimensional space, and iv) anycombination of these three, resulting from the inputted set of atomproperties.

The modules operate similarly as trained and discussed herein. However,the flow of information starts at the atom properties module and thenthrough the atom vector module, to the 3D vector field module, up to theagreement comparator module, down to the 3D vector field module and themolecule vector module, and finally through the molecule propertypredictor module.

C. The modules can cooperate to perform an operation 1) to receive aninput of a set of atoms via i) the atom and molecule encoder or ii)directly supplied to the atom vector module manually by a user and then2) from the molecule property predictor module to present in the outputmodule i) a resulting molecule, ii) a composition of atoms in theresulting molecule, iii) a set of properties for the resulting molecule,including the structure of the molecule in 3-dimensional space, and iv)any combination of these three, resulting from the inputted set ofatoms.

The modules operate similarly as trained and discussed herein. However,the flow of information starts at the atom properties module and thenthrough the atom vector module, to the 3D vector field module, up to theagreement comparator module, down to the 3D vector field module and themolecule vector module, and finally through the molecule propertypredictor module.

D. In a reverse flow through the modules, the modules can cooperate toperform an operation 1) to receive an input of a molecule via i) theatom and molecule encoder or ii) manually to the molecule vector module,and then 2) from the atom properties module to present in the outputmodule i) a resulting composition of the atoms for that molecule, ii) aset of properties for each of the atoms, including each atom'sarrangement/position in 3-dimensional space, and iii) any combination ofthese two, resulting from the inputted molecule.

The modules operate similarly as trained and discussed herein. However,the flow of information starts at the molecule vector module, to the 3Dvector field module, up to the agreement comparator module, down to the3D vector field module and the atom vector module, and finally throughthe atom properties module.

FIG. 7 illustrates an embodiment of a diagram of an example atom vectormodule configured to cooperate with one or more transformers, such as a3D vector field module, to map atom vectors to a 3-dimensional space.

The machine learning algorithm can assign vectors to objects making upthe atom and/or the set of atoms in order to satisfy asserted relations.The vectors can indicate, for example, a double bond exists between theoxygen atom and neighboring carbon atom. A single bond exists betweenthe nitrogen, oxygen and carbon atoms at positions b+c+d. Note, singlebonded (a) and double bonded (a) refer to the bonds to the atom (e.g.Carbon) at “a”. Each atom, such as the nitrogen atom, the first carbonatom, and the second carbon atom can be treated as objects representedwith vectors. The vectors can be a sequence of numbers and or symbols.For example, the vector value (3, 5, 7) indicates a point (e.g. an X, Y,Z coordinate) in a 3D space. (see for example FIG. 8 )

Next, the Artificial Intelligence engine 100 can construct, for example,a neural network structured on expert knowledge and then can train theneural network based on empirical data. The Artificial Intelligenceengine 100 can use logical operators for the nodes in the constructedneural networks. The Artificial Intelligence engine 100 can haverelations represented as sub-networks in the neural networks.

FIG. 8 shows a mapping of atom vectors to a 3-dimensional space as wellas mapped to molecule vectors to the 3-dimensional space with a samecoordinate system to allow for a proper comparison of the atom vectorsto the molecule vectors because a molecule is a 3-dimensional object andan arrangement of the atoms within that 3-dimensional space. In thisexample, the semantic vector field of the candidate molecule and itsgeometric structure is displayed. Every (x, y, z) vector point in 3Dspace can get a distance-dependent sum of the atom vectors. Each vectorpoint in 3D space gets displayed. The machine learning model may selecta 2D surface from 3D space based on reference to nearest atoms. In anexample, the machine learning model maps the vectors to colors bysimilarity.

The machine learning model has been trained to encode the moleculevectors and their 3D relationships between atom vectors.

The machine learning model has been trained to reconstruct moleculevectors in the space by comparing them to atom vectors generated by theatoms (and vice versa). When creating the atom vector field 106, aportion of the atom vector values such as (x2, y2, z2) coordinates helpproject the atoms into 3-dimensional space.

The machine learning model maps, for example, five separate exampleatoms and/or set of atoms of carbon, oxygen, nitrogen, and others intoan atom vector field 106 constructed from atom vector values. Themachine learning model maps an example molecule and its molecule vectorfield (that has carbon, oxygen, nitrogen, and others) to the fiveseparate example atoms and/or set of atoms as illustrated. In this way,a molecule (made up atoms and/or sets of atoms) is reconstructed from amolecule vector value that approximately agrees with the atom vectorfield 106. In this example, the atom vectors in the first, third, andfifth boxes almost exactly match up from atom vectors to moleculevectors as well as the second and fourth boxes have the same atoms butdiffer between the molecule vectors and atom vectors in 3D space. Note,in this case, the comparison would still loosely agree and thus beapproximately the same.

Again, the agreement comparator module has been trained to use machinelearning algorithms, such as gradient descent, to iteratively compare aninstance of the atom vector field 106 to a molecule vector field in a 3Ddimension space (and vice versa molecule vector field to atom vectorfield 106) and determine if they are approximately the same. Again, thestructure of the molecule formed by atoms in the 3 dimensional space iscompared to atom vectors in 3 dimensional space.

The machine learning network in the agreement comparator module istrained to look at a comparison of atom vectors to molecule vectors, andthen include loose matches as a potential positive agreement of thosetwo vectors in 3-dimensional space. Thus, loose approximate matches ofatom vectors to molecule vectors can be satisfactory, (rather than aneed for exact matches). This helps the agreement comparator moduledetermine something unknown (such as a hypothetical molecule that shouldhave a desired set of target properties) in the semantic vector space.The semantic vector fields of the atoms vectors and molecule vector canbe approximately the same (roughly in the same neighborhood), whichmeans they will have approximately the same molecular properties.

In an embodiment, the machine learning network in the agreementcomparator module can use an imaging algorithm to match up ‘X’ amount ofvector points, such as 5 points, between the two vector fields to see ifthey approximately match up. When, for example, a majority of thecompared vector points match up and the remainder are close in theneighborhood, then the atom vectors will be considered to approximatelymatch the molecule vectors.

FIG. 9 illustrates an embodiment of a diagram of an example input modulethat is configured to receive and supply information from one or moreknowledge sources, including at least information from a chemistryexpert, to act as one or more constraints to adapt the machine learningto find a candidate molecule vector field to agree with an atom vectorfield 106 in order to fit the constraints created by the information.

Chemists may have intuition about structures for a given target toconstrain semantic vector field of properties of the molecule in 3Dspace, such as a ring structure needs to be present, and/or a certaingeometric relation to each other needs to be present, and/or certainatoms must be paired, etc. The neural network can use the constraintsfrom the knowledge sources when learning to recognize these patterns andrelationships in 3D space and corresponding molecule vectors havingthese 3D geometric relationships. Thus, the machine learning can utilizethese relationship constraints put in by the chemistry expert whilestill trying to solve for molecule vectors that meet substantially theknown values of atom properties and/or target/desired properties of themolecule and corresponding molecule vectors.

Next, a set of targeted properties from a user can be supplied to amolecule property predictor module as an output target for a machinelearning algorithm to achieve when trying to find the one or morecandidate molecules that satisfy the set of targeted properties. Themachine learning process iteratively supplies candidate molecular vectorvalues into the property predictor module until one or more candidatemolecular vector values are found that satisfy the supplied targetedproperties (goal for the learning algorithm) for the unnamed molecule.After finding each candidate molecular vector value that satisfies thesupplied targeted properties for the unnamed molecule, then transformingeach candidate molecular vector value into a 3-dimensional vector fieldfor that candidate molecular vector value that indicates the structureof the candidate molecule. Thus, the Artificial Intelligence engine 100is coded with intelligence in an automated fashion for the machinelearning to logically adapt the molecular design of target properties tomolecules in existence or even still a hypothetical molecule yet to befound as naturally occurring in nature and/or created in a laboratorybut has a similar geometric structure to previously learned geometricstructures corresponding to the set of targeted properties.

FIG. 10 illustrates an embodiment of a diagram of an example ArtificialIntelligence engine recognizing patterns of structures in molecules sothat molecular properties can be correlated to structural properties inthe molecule. The Artificial Intelligence engine 100 uses a machinelearning module trained to iteratively submit molecule vector valuescorresponding to a geometric structure of one or more candidatemolecules in 3-dimensional space. The molecule vector values correspondto the geometric structure of the one or more candidate molecules. Thesemolecule vector values and structures can include a merely hypotheticalmolecule yet to be found as naturally occurring in nature and/or createdin a laboratory. The molecule vector values are iteratively submittedfrom the molecule vector module to the molecule property predictor tocheck when a first candidate molecule is found that satisfies the set oftargeted properties.

In an embodiment, an example neural network applies a computer visiontechnique in 3D space to train the neural network to recognize moieties.As discussed, the neural network naturally generalizes to molecules thatwere not captured by the structure-based rules. The neural network wastrained to learn to recognize moieties/portion of a molecule (e.g.groups of atoms) with a particular function and structure to thatportion of a molecule and then label each distinct pattern of atomstructures as its own region in the semantic vector field. For example,the trained neural network characterizes each different moiety of atomswith either an ellipsoid in 3D space, and/or a ring of particular atomswith a circle in 3D space, etc. Each geometric structure can later becorrelated to a property of the molecule. These moieties have similarpatterns and relationships but do not have to exactly match the originalmolecule and its exact pattern. As a characteristic of the machinelearning, the machine learning model learns to recognize patterns andthen parameters, values, and effects associated with those patterns. Themachine learning model naturally can characteristically generalize thisunderstanding to sets of atoms with similar structure patterns that maynot have been trained on.

FIG. 11 illustrates an embodiment of a diagram of an example ArtificialIntelligence engine that can generate physics-based, vectorrepresentations for testing and predicting molecules.

The Artificial Intelligence engine 100 generates a representation of thecandidate molecule and its geometric structure including the atomscomposing the candidate molecule that has the targeted properties;rather than, just predicting or performing a look-up function for theproperties of a molecule known to naturally occur in nature. TheArtificial Intelligence engine 100 supplies the representation of thecandidate molecule and its geometric structure, including the atomscomposing the candidate molecule that has the targeted properties, tothe user on 1) a display screen, 2) in a printout, 3) as an electronicmessage, (e.g. email, text, instant message, etc.) sent to an account ofthe user, and 4) any combination these three.

The Artificial Intelligence engine 100 can cooperate with a database ofmolecules and properties of molecules including the geometric structure.The Artificial Intelligence engine 100 in response to a query on a setof targeted properties submitted by a user, can determine whether does amolecule in the existing molecule database satisfy the set of targetedproperties? If yes, send the molecule and its geometric structure to theuser. If not, go through the trained modules and evaluate with themachine learning models for one or more candidate molecules that canhave the set of targeted properties for molecules. When looking forcandidate molecules to satisfy the set of targeted properties, then theArtificial Intelligence engine 100 can extract information for variousdata sources to pull in information to act as constraints on the machinelearning process to find a matching candidate molecule as well as aninformation source on the geometric structure of atoms and molecules.The Artificial Intelligence engine 100 is coded with intelligence todetermine the appropriate data source to query and extract data fromthat particular data source. A candidate molecule satisfying the set oftargeted properties can also have its atomic composition and geometricstructure sent to be synthesized and tested.

Network

FIG. 12 illustrates a diagram of a number of electronic systems anddevices communicating with each other in a network environment inaccordance with an embodiment of the Artificial Intelligence engine. Thenetwork environment 800 has a communications network 820. The network820 can include one or more networks selected from an optical network, acellular network, the Internet, a Local Area Network (“LAN”), a WideArea Network (“WAN”), a satellite network, a fiber network, a cablenetwork, and combinations thereof. In an embodiment, the communicationsnetwork 820 is the Internet. As shown, there may be many servercomputing systems and many client computing systems connected to eachother via the communications network 820. However, it should beappreciated that, for example, a single client computing system can alsobe connected to a single server computing system. Thus, any combinationof server computing systems and client computing systems may connect toeach other via the communications network 820.

The system containing the Artificial Intelligence engine 100 can use anetwork like this to supply training data to create and train a machinelearning network. The Artificial Intelligence engine 100 can also resideand be implemented in this network environment, for example, in thecloud platform of server 804A and database 806A, a local server 804B anddatabase 806B, on a device such as laptop 802D, in a smart system suchas smart automobile 802D, and other similar platforms.

The communications network 820 can connect one or more server computingsystems selected from at least a first server computing system 804A anda second server computing system 804B to each other and to at least oneor more client computing systems as well. The server computing system804A can be, for example, the one or more server systems 220. The servercomputing systems 804A and 804B can each optionally include organizeddata structures such as databases 806A and 806B. Each of the one or moreserver computing systems can have one or more virtual server computingsystems, and multiple virtual server computing systems can beimplemented by design. Each of the one or more server computing systemscan have one or more firewalls to protect data integrity.

The at least one or more client computing systems can be selected from afirst mobile computing device 802A (e.g., smartphone with anAndroid-based operating system), a second mobile computing device 802E(e.g., smartphone with an iOS-based operating system), a first wearableelectronic device 802C (e.g., a smartwatch), a first portable computer802B (e.g., laptop computer), a third mobile computing device or secondportable computer 802F (e.g., tablet with an Android- or iOS-basedoperating system), a smart device or system incorporated into a firstsmart automobile 802D, a smart device or system incorporated into afirst smart bicycle, a first smart television 802H, a first virtualreality or augmented reality headset 804C, and the like. The clientcomputing system 802B can be, for example, one of the one or more clientsystems 210, and any one or more of the other client computing systems(e.g., 802A, 802C, 802D, 802E, 802F, 802G, 802H, and/or 804C) caninclude, for example, the software application or the hardware-basedsystem in which the training of the Artificial Intelligence can occurand/or can be deployed into. Each of the one or more client computingsystems can have one or more firewalls to protect data integrity.

It should be appreciated that the use of the terms “client computingsystem” and “server computing system” is intended to indicate the systemthat generally initiates a communication and the system that generallyresponds to the communication. For example, a client computing systemcan generally initiate a communication and a server computing systemgenerally responds to the communication. No hierarchy is implied unlessexplicitly stated. Both functions can be in a single communicatingsystem or device, in which case, the client-server and server-clientrelationship can be viewed as peer-to-peer. Thus, if the first portablecomputer 802B (e.g., the client computing system) and the servercomputing system 804A can both initiate and respond to communications,their communications can be viewed as peer-to-peer. Additionally, theserver computing systems 804A and 804B include circuitry and softwareenabling communication with each other across the network 820. Server804B may send, for example, simulator data to server 804A.

Any one or more of the server computing systems can be a cloud provider.A cloud provider can install and operate application software in a cloud(e.g., the network 820 such as the Internet) and cloud users can accessthe application software from one or more of the client computingsystems. Generally, cloud users that have a cloud-based site in thecloud cannot solely manage a cloud infrastructure or platform where theapplication software runs. Thus, the server computing systems andorganized data structures thereof can be shared resources, where eachcloud user is given a certain amount of dedicated use of the sharedresources. Each cloud user's cloud-based site can be given a virtualamount of dedicated space and bandwidth in the cloud. Cloud applicationscan be different from other applications in their scalability, which canbe achieved by cloning tasks onto multiple virtual machines at run-timeto meet changing work demand. Load balancers distribute the work overthe set of virtual machines. This process is transparent to the clouduser, who sees only a single access point.

Cloud-based remote access can be coded to utilize a protocol, such asHypertext Transfer Protocol (“HTTP”), to engage in a request andresponse cycle with an application on a client computing system such asa web-browser application resident on the client computing system. Thecloud-based remote access can be accessed by a smartphone, a desktopcomputer, a tablet, or any other client computing systems, anytimeand/or anywhere. The cloud-based remote access is coded to engage in 1)the request and response cycle from all web browser-based applications,3) the request and response cycle from a dedicated on-line server, 4)the request and response cycle directly between a native applicationresident on a client device and the cloud-based remote access to anotherclient computing system, and 5) combinations of these.

In an embodiment, the server computing system 804A can include a serverengine, a web page management component or direct application component,a content management component, and a database management component. Theserver engine can perform basic processing and operating-system leveltasks. The web page management component can handle creation and displayor routing of web pages or screens associated with receiving andproviding digital content and digital advertisements, through a browser.Likewise, the direct application component may work with a client appresident on a user's device. Users (e.g., cloud users) can access one ormore of the server computing systems by means of a Uniform ResourceLocator (“URL”) associated therewith. The content management componentcan handle most of the functions in the embodiments described herein.The database management component can include storage and retrievaltasks with respect to the database, queries to the database, and storageof data.

In an embodiment, a server computing system can be configured to displayinformation in a window, a web page, or the like. An applicationincluding any program modules, applications, services, processes, andother similar software executable when executed on, for example, theserver computing system 804A, can cause the server computing system 804Ato display windows and user interface screens in a portion of a displayscreen space.

Each application has a code scripted to perform the functions that thesoftware component is coded to carry out such as presenting fields totake details of desired information. Algorithms, routines, and engineswithin, for example, the server computing system 804A can take theinformation from the presenting fields and put that information into anappropriate storage medium such as a database (e.g., database 806A). Acomparison wizard can be scripted to refer to a database and make use ofsuch data. The applications may be hosted on, for example, the servercomputing system 804A and served to the specific application or browserof, for example, the client computing system 802B. The applications thenserve windows or pages that allow entry of details.

Computing Systems

FIG. 13 illustrates a diagram of an embodiment of one or more computingdevices that can be a part of the systems associated with the ArtificialIntelligence engine discussed herein. The computing device 900 mayinclude one or more processors or processing units 920 to executeinstructions, one or more memories 930-932 to store information, one ormore data input components 960-963 to receive data input from a user ofthe computing device 900, one or more modules that include themanagement module, a network interface communication circuit 970 toestablish a communication link to communicate with other computingdevices external to the computing device, one or more sensors where anoutput from the sensors is used for sensing a specific triggeringcondition and then correspondingly generating one or more preprogrammedactions, a display screen 991 to display at least some of theinformation stored in the one or more memories 930-932 and othercomponents. Note, portions of this system that are implemented insoftware 944, 945, 946 may be stored in the one or more memories 930-932and are executed by the one or more processors 920.

The system memory 930 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read-only memory (ROM) 931and random access memory (RAM) 932. These computing machine-readablemedia can be any available media that can be accessed by computingsystem 900. By way of example, and not limitation, computingmachine-readable media use includes storage of information, such ascomputer-readable instructions, data structures, other executablesoftware, or other data. Computer-storage media includes, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other tangible medium which can be usedto store the desired information and which can be accessed by thecomputing device 900. Transitory media such as wireless channels are notincluded in the machine-readable media.

The system further includes a basic input/output system 933 (BIOS)containing the basic routines that help to transfer information betweenelements within the computing system 900, such as during start-up, istypically stored in ROM 931. RAM 932 typically contains data and/orsoftware that are immediately accessible to and/or presently beingoperated on by the processing unit 920. By way of example, and notlimitation, the RAM 932 can include a portion of the operating system934, application programs 935, other executable software 936, andprogram data 937.

The computing system 900 can also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only, thesystem has a solid-state memory 941. The solid-state memory 941 istypically connected to the system bus 921 through a non-removable memoryinterface such as interface 940, and USB drive 951 is typicallyconnected to the system bus 921 by a removable memory interface, such asinterface 950.

A user may enter commands and information into the computing system 900through input devices such as a keyboard, touchscreen, or software orhardware input buttons 962, a microphone 963, a pointing device and/orscrolling input component, such as a mouse, trackball or touch pad.These and other input devices are often connected to the processing unit920 through a user input interface 960 that is coupled to the system bus921, but can be connected by other interface and bus structures, such asa parallel port, game port, or a universal serial bus (USB). A displaymonitor 991 or other type of display screen device is also connected tothe system bus 921 via an interface, such as a display interface 990. Inaddition to the monitor 991, computing devices may also include otherperipheral output devices such as speakers 997, a vibrator 999, andother output devices, which may be connected through an outputperipheral interface 995.

The computing system 900 can operate in a networked environment usinglogical connections to one or more remote computers/client devices, suchas a remote computing system 980. The remote computing system 980 can apersonal computer, a mobile computing device, a server, a router, anetwork PC, a peer device or other common network node, and typicallyincludes many or all of the elements described above relative to thecomputing system 900. The logical connections can include a personalarea network (PAN) 972 (e.g., Bluetooth®), a local area network (LAN)971 (e.g., Wi-Fi), and a wide area network (WAN) 973 (e.g., cellularnetwork), but may also include other networks such as a personal areanetwork (e.g., Bluetooth®). Such networking environments are commonplacein offices, enterprise-wide computer networks, intranets and theInternet. A browser application may be resonant on the computing deviceand stored in the memory.

When used in a LAN networking environment, the computing system 900 isconnected to the LAN 971 through a network interface 970, which can be,for example, a Bluetooth® or Wi-Fi adapter. When used in a WANnetworking environment (e.g., Internet), the computing system 900typically includes some means for establishing communications over theWAN 973. With respect to mobile telecommunication technologies, forexample, a radio interface, which can be internal or external, can beconnected to the system bus 921 via the network interface 970, or otherappropriate mechanism. In a networked environment, other softwaredepicted relative to the computing system 900, or portions thereof, maybe stored in the remote memory storage device. By way of example, andnot limitation, the system has remote application programs 985 asresiding on remote computing device 980. It will be appreciated that thenetwork connections shown are examples and other means of establishing acommunications link between the computing devices that may be used.

As discussed, the computing system 900 can include mobile devices with aprocessing unit 920, a memory (e.g., ROM 931, RAM 932, etc.), a built-inbattery to power the computing device, an AC power input to charge thebattery, a display screen, a built-in Wi-Fi circuitry to wirelesslycommunicate with a remote computing device connected to the network.

It should be noted that the present design can be carried out on acomputing system such as that described with respect to shown herein.However, the present design can be carried out on a server, a computingdevice devoted to message handling, or on a distributed system in whichdifferent portions of the present design are carried out on differentparts of the distributed computing system.

In some embodiments, software used to facilitate algorithms discussedherein can be embedded onto a non-transitory machine-readable medium. Amachine-readable medium includes any mechanism that stores informationin a form readable by a machine (e.g., a computer). For example, anon-transitory machine-readable medium can include read-only memory(ROM); random access memory (RAM); magnetic disk storage media; opticalstorage media; flash memory devices; Digital Versatile Disc (DVD's),EPROMs, EEPROMs, FLASH memory, magnetic or optical cards, or any type ofmedia suitable for storing electronic instructions.

Note, an application described herein includes but is not limited tosoftware applications, mobile applications, and programs that are partof an operating system application. Some portions of this descriptionare presented in terms of algorithms and symbolic representations ofoperations on data bits within a computer memory. These algorithmicdescriptions and representations are the means used by those skilled inthe data processing arts to most effectively convey the substance oftheir work to others skilled in the art. An algorithm is here, andgenerally, conceived to be a self-consistent sequence of steps leadingto a desired result. The steps are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like. These algorithms canbe written in a number of different software programming languages suchas C, C+, HTTP, Java, Python, or other similar languages. Also, analgorithm can be implemented with lines of code in software, configuredlogic gates in software, or a combination of both. In an embodiment, thelogic consists of electronic circuits that follow the rules of BooleanLogic, software that contain patterns of instructions, or anycombination of both. Any portions of an algorithm implemented insoftware can be stored in an executable format in a portion of a memoryand is executed by one or more processors. In an embodiment, a modulecan be implemented in electronic hardware such as logic and otherelectronic components to perform the functions discussed for thatmodule, software as a block of executable code coded to perform thefunctions discussed for that module, and/or a combination of softwarecooperating with electronic hardware.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussions, itis appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers, or other suchinformation storage, transmission or display devices.

Many functions performed by electronic hardware components can beduplicated by software emulation. Thus, a software program written toaccomplish those same functions can emulate the functionality of thehardware components in input-output circuitry. Thus, provided herein areone or more non-transitory machine-readable medium configured to storeinstructions and data that when executed by one or more processors onthe computing device of the foregoing system, causes the computingdevice to perform the operations outlined as described herein.

References in the specification to “an embodiment,” “an example”, etc.,indicate that the embodiment or example described may include aparticular feature, structure, or characteristic, but every embodimentmay not necessarily include the particular feature, structure, orcharacteristic. Such phrases can be not necessarily referring to thesame embodiment. Further, when a particular feature, structure, orcharacteristic is described in connection with an embodiment, it isbelieved to be within the knowledge of one skilled in the art to affectsuch feature, structure, or characteristic in connection with otherembodiments whether or not explicitly indicated.

While the foregoing design and embodiments thereof have been provided inconsiderable detail, it is not the intention of the applicant(s) for thedesign and embodiments provided herein to be limiting. Additionaladaptations and/or modifications are possible, and, in broader aspects,these adaptations and/or modifications are also encompassed.Accordingly, departures may be made from the foregoing design andembodiments without departing from the scope afforded by the followingclaims, which scope is only limited by the claims when appropriatelyconstrued.

What is claimed is:
 1. An apparatus, comprising: an ArtificialIntelligence engine composed of two or more modules that use one or moremachine learning models; where a molecule property predictor module isconfigured to cooperate with a molecule vector module to predictmolecular properties from a molecule vector field in a 3-dimensionalspace; and where an input module is configured to cooperate with themolecule vector module, the molecule property predictor module, and anatom vector module, to allow these modules to apply the one or moremachine learning models to allow a user 1) to input two or more atomsand have a first machine learning model in the molecule propertypredictor module output a set of properties for a resulting moleculeformed by the two or more atoms, as well as 2) to supply an input of aset of targeted properties for an unlabeled molecule and have the firstmachine learning model output a set of atoms and a geometric structurefor the unlabeled molecule.
 2. The apparatus of claim 1, furthercomprising: an agreement comparator module having a second machinelearning model trained to compare an atom vector field to the moleculevector field in the 3-dimensional space and determine when they areapproximately the same, where the geometric structure of the unlabeledmolecule is formed by the set of atoms in the 3-dimensional space. 3.The apparatus of claim 1, where the atom vector module and the moleculevector module are configured to cooperate with one or more transformersto map atom vectors to the 3-dimensional space as well as to mapmolecule vectors to the 3-dimensional space with a same coordinatesystem to allow for a proper comparison of the atom vectors to themolecule vectors.
 4. The apparatus of claim 1, where the AI engine isconfigured to receive and use information from one or more knowledgesources, including at least information from a chemistry expert, to actas one or more constraints to adapt a machine learning to find acandidate molecule vector field to agree with an atom vector field inorder to fit the constraints created by the information.
 5. Theapparatus of claim 1, where the first machine learning model in themolecule property predictor module is configured to learn to predictmolecular properties of molecule, under analysis, to values ofstructural properties for that molecule under analysis by having amolecule semantic dependent vector field in each space of the moleculeunder analysis in the 3-dimensional space.
 6. The apparatus of claim 1,where an atom properties module is configured to cooperate with an atomvector module to bilaterally predict atom properties to atom vectorvalues so that an output layer of the atom properties module willproduce i) an identity of an atom and ii) values of the atom propertiesfor a supplied atom vector value.
 7. A non-transitory computer-readablemedium including executable instructions that, when executed with one ormore processors, cause an Artificial Intelligence engine to performoperations as follows, comprising: using a molecule property predictormodule to cooperate with a molecule vector module to predict molecularproperties from a molecule vector field in a 3D space, and using aninput module to cooperate with the molecule vector module, the moleculeproperty predictor module, and an atom vector module, to allow thesemodules to apply the one or more machine learning models to allow auser 1) to put in two or more atoms and have a first machine learningmodel in the molecule property predictor module output a set ofproperties for a resulting molecule formed by the two or more atoms aswell as 2) to supply an input of a set of targeted properties for anunlabeled molecule and have the first machine learning model output aset of atoms and a geometric structure for the unlabeled molecule. 8.The non-transitory computer-readable medium of claim 7 including furtherexecutable instructions that, when executed with the one or moreprocessors, cause the Artificial Intelligence engine to perform furtheroperations, comprising: using an agreement comparator module trainedwith a machine learning algorithm to compare an atom vector field to themolecule vector field in the 3-dimensional space and determine when theyare approximately a same, where the geometric structure of the unlabeledmolecule is formed by the set of atoms in the 3-dimensional space. 9.The non-transitory computer-readable medium of claim 7 including furtherexecutable instructions that, when executed with the one or moreprocessors, cause the Artificial Intelligence engine to perform furtheroperations, comprising: using the atom vector module and the moleculevector module to cooperate with one or more transformers in order to mapatom vectors to the 3-dimensional space as well as to map moleculevectors to the 3-dimensional space with a same coordinate system toallow for a proper comparison of the atom vectors to the moleculevectors.
 10. The non-transitory computer-readable medium of claim 7including further executable instructions that, when executed with theone or more processors, cause the Artificial Intelligence engine toperform further operations, comprising: using the input module toreceive and supply information from knowledge sources, including atleast information from an expert source, to act as one or moreconstraints to adapt a machine learning to find a candidate moleculevector field to agree with an atom vector field in the 3-dimensionalspace to fit the constraints created by the information.
 11. A method ofusing an Artificial Intelligence engine, comprising: submitting a queryto the Artificial Intelligence engine to search directly for a set oftargeted properties for an unnamed molecule having the set of targetedproperties; generating an indication of a structure of one or morecandidate molecules found to have the set of targeted properties withthe Artificial Intelligence engine by applying one or more machinelearning algorithms; and supplying the indication of the structure ofthe one or more candidate molecules found to satisfy the set of targetedproperties in 3-dimensional space to a user in response to the query forthe set of targeted properties to the Artificial Intelligence engine.12. The method of using an Artificial Intelligence engine of claim 11,further comprising: reconstructing i) a 3-dimensional vector field of astructure of a first candidate molecule found that satisfies the set oftargeted properties ii) with a structure of atom vectors from one ormore atoms in a same 3-dimensional space.
 13. The method of using anArtificial Intelligence engine of claim 11, further comprising: using amachine learning model trained to iteratively submit molecule vectorvalues corresponding a geometric structure of one or more candidatemolecules in 3-dimensional space, where the molecule vector valuescorresponding to the geometric structure of the one or more candidatemolecules can include a merely hypothetical molecule yet to be found asnaturally occurring in nature and/or created in a laboratory; and wherethe molecule vector values are iteratively submitted from the moleculevector module to the molecule property predictor to check when a firstcandidate molecule is found that satisfies the set of targetedproperties.
 14. The method of using an Artificial Intelligence engine ofclaim 13, further comprising: generating a representation of the firstcandidate molecule and its geometric structure including the atomscomposing the first candidate molecule that has the targeted properties;rather than, just predicting or performing a look-up function for theproperties of a molecule known to naturally occur in nature; andsupplying the representation of the first candidate molecule and itsgeometric structure including the atoms composing the first candidatemolecule that has the targeted properties to the user on 1) a displayscreen, 2) in a print out, 3) as an electronic message sent to anaccount of the user, and 4) any combination these three.
 15. The methodof using an Artificial Intelligence engine of claim 11, furthercomprising: receiving an input of the set of targeted properties for theunnamed molecule from the user, which is supplied to a molecule propertypredictor module as an output target for a first machine learningalgorithm to achieve when trying to find the one or more candidatemolecules that satisfy the set of targeted properties; 2) iterativelysupplying candidate molecular vector values into the property predictormodule until one or more candidate molecular vector values are foundthat satisfy the supplied targeted properties for the unnamed molecule;and after finding a first candidate molecular vector value thatsatisfies the supplied targeted properties for the unnamed molecule,then transforming the first candidate molecular vector value into a3-dimensional vector field for the first candidate molecular vectorvalue that indicates the structure of the first candidate molecule. 16.A non-transitory computer-readable medium including executableinstructions that, when executed with one or more processors, cause anArtificial Intelligence engine to perform operations as follows,comprising: submitting a query to the Artificial Intelligence engine tosearch directly for a set of targeted properties for an unnamed moleculehaving the set of targeted properties; generating an indication of astructure of one or more candidate molecules found to have the set oftargeted properties with the Artificial Intelligence engine by applyingone or more machine learning algorithms; and supplying the indication ofthe structure of the one or more candidate molecules found to satisfythe set of targeted properties in 3-dimensional space to a user inresponse to the query for the set of targeted properties to theArtificial Intelligence engine.
 17. The non-transitory computer-readablemedium of claim 16 including further executable instructions that, whenexecuted with the one or more processors, cause the ArtificialIntelligence engine to perform further operations, comprising:reconstructing i) a 3-dimensional vector field of a structure of a firstcandidate molecule found that satisfies the set of targeted propertiesii) with a structure of atom vectors from one or more atoms in a same3-dimensional space.
 18. The non-transitory computer-readable medium ofclaim 16 including further executable instructions that, when executedwith the one or more processors, cause the Artificial Intelligenceengine to perform further operations, comprising: using a machinelearning model trained to iteratively submit molecule vector valuescorresponding a geometric structure of one or more candidate moleculesin 3-dimensional space, where the molecule vector values correspondingto the geometric structure of the one or more candidate molecules caninclude a merely hypothetical molecule yet to be found as naturallyoccurring in nature and/or created in a laboratory; and where themolecule vector values are iteratively submitted from the moleculevector module to the molecule property predictor to check when a firstcandidate molecule is found that satisfies the set of targetedproperties.
 19. The non-transitory computer-readable medium of claim 18including further executable instructions that, when executed with theone or more processors, cause the Artificial Intelligence engine toperform further operations, comprising: generating a representation ofthe first candidate molecule and its geometric structure including theatoms composing the first candidate molecule that has the targetedproperties; rather than, just predicting or performing a look-upfunction for the properties of a molecule known to naturally occur innature; and supplying the representation of the first candidate moleculeand its geometric structure including the atoms composing the firstcandidate molecule that has the targeted properties to the user on 1) adisplay screen, 2) in a print out, 3) as an electronic message sent toan account of the user, and 4) any combination these three.
 20. Thenon-transitory computer-readable medium of claim 16 including furtherexecutable instructions that, when executed with the one or moreprocessors, cause the Artificial Intelligence engine to perform furtheroperations, comprising: receiving an input of the set of targetedproperties for the unnamed molecule from the user, which is supplied toa molecule property predictor module as an output target for a firstmachine learning algorithm to achieve when trying to find the one ormore candidate molecules that satisfy the set of targeted properties; 2)iteratively supplying candidate molecular vector values into theproperty predictor module until one or more candidate molecular vectorvalues are found that satisfy the supplied targeted properties for theunnamed molecule; and after finding a first candidate molecular vectorvalue that satisfies the supplied targeted properties for the unnamedmolecule, then transforming the first candidate molecular vector valueinto a 3-dimensional vector field for the first candidate molecularvector value that indicates the structure of the first candidatemolecule.